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We address the problem of visual knowledge adaptation by leveraging labeled patterns from source domain and a very limited number of labeled instances in target domain to learn a robust classifier for visual categorization. This paper proposes a new extreme learning machine (ELM)-based cross-domain network learning framework, that is called ELM-based Domain Adaptation (EDA). It allows us to learn...
In this paper, we propose a l2,1-norm based discriminative robust transfer learning (DKTL) method for domain adaptation tasks. The key idea is to simultaneously learn discriminative subspaces by using the proposed domain-class-consistency (DCC) metric, and the representation based robust transfer model between source domain and target domain via l21-norm minimization. The DCC metric includes two parts:...
An object recognition method based on Gabor wavelet and SVM is proposed in this paper. First features of the object are extracted by using Gabor wavelet, and then the dimensions of the Gabor features are reduced with Principal Component Analysis, and finally classification is performed with Support Vector Machine. And this method is applied to the Columbia image library COIL-20 for experiments. Compared...
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